基于多任务循环神经网络带状回归模型的乳腺癌个体生存分析

陈睿; 蔡念; 罗智浩; 刘璇; 黎剑

doi:10.12052/gdutxb.230007

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基于多任务循环神经网络带状回归模型的乳腺癌个体生存分析

智慧医疗 | 更新时间：2026-05-27

- 基于多任务循环神经网络带状回归模型的乳腺癌个体生存分析
- Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model
- 广东工业大学学报 2024年41卷第1期页码：34-40
- 作者机构：
  
  1. 广东工业大学信息工程学院,广东,广州,510006
  2. 中山大学肿瘤防治中心华南肿瘤学国家重点实验室,广东,广州,510060
  3. 中山大学肿瘤防治中心广东省恶性肿瘤临床医学研究中心,广东,广州,510060
- 作者简介：
  
  陈睿(1998–)，男，硕士研究生，主要研究方向为机器学习、生存分析等
  蔡念(1976–)，男，教授，博士，主要研究方向为机器视觉、机器学习、数字信号处理等，E-mail：cainian@sina.com
- 基金信息：
  
  国家自然科学基金资助面上项目(82172019);广州市科技计划项目 (202102010251)
- DOI：10.12052/gdutxb.230007
  中图分类号： TP391
- 网络首发：2024-01-25，
  
  纸质出版：2024
- 稿件说明：
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陈睿, 蔡念, 罗智浩, 刘璇, 黎剑. 基于多任务循环神经网络带状回归模型的乳腺癌个体生存分析[J]. 广东工业大学学报, 2024, 41(1): 34-40.

Chen Rui, Cai Nian, Luo Zhi-hao, et al. Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model[J]. 2024, 41(1): 34-40.
陈睿, 蔡念, 罗智浩, 刘璇, 黎剑. 基于多任务循环神经网络带状回归模型的乳腺癌个体生存分析[J]. 广东工业大学学报, 2024, 41(1): 34-40. DOI： 10.12052/gdutxb.230007.

Chen Rui, Cai Nian, Luo Zhi-hao, et al. Individual Survival Analysis of Breast Cancer Based on Multi-task Recurrent Neural Network Banded Regression Model[J]. 2024, 41(1): 34-40. DOI： 10.12052/gdutxb.230007.

摘要

针对乳腺癌病程长、疾病发展较缓和的特点，提出了一种多任务循环神经网络带状回归模型进行乳腺癌个体生存分析。首先，提出一种基于循环神经网络的多任务带状回归模型，通过识别各病理特征对不同患者之间影响的区别，优化患者个体生存分析。其次，对带状校验矩阵的形式进行拓展并研究其对患者风险分布的影响。最后，在乳腺癌真实数据集上进行生存分析，不同患者之间产生明显的差异性，验证了模型的有效性。在2个乳腺癌真实数据集上进行的生存分析结果显示，基于循环神经网络的多任务带状回归模型的一致性指数(Concordance Index

C-index)较医学上常用的Cox回归模型有较大提升，并有着更小的95%置信区间。

Abstract

In view of the characteristics of long course and mild disease development of breast cancer

a multi-task recurrent neural network banded regression model is proposed to analyze individual survival of breast cancer. First

a multi-task banded regression model based on recurrent neural network is proposed to optimize the individual survival analysis of patients by identifying the difference in the effects of various pathological features on different patients. Then

the form of the banded check matrix is expanded and its effect on the hazard distribution of patient is investigated. Finally

the survival analysis on the real datasets of breast cancer shows obvious differences among different patients

which verifies the validity of the model. The survival analysis on two real datasets of breast cancer shows that the C-index of the multi-task banded regression model based on recurrent neural network is greatly improved compared with the Cox regression model commonly used in medicine

and has a smaller 95% confidence interval.

关键词

Keywords

references

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